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NOE ( Network Of Excellence ) Project
Project for Fostering and Promoting
Statistical Thinking

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■ History—

Message from Director-General

The Institute of Statistical Mathematics (ISM) celebrated its 75th anniversary on June 5, 2019. On this day, the anniversary ceremony, symposium, Open Campus Day presentation, and other programs targeted at
the public were held at the Hitotsubashi Auditorium, Chiyoda-ku, Tokyo,
attracting more than 650 attendants. It was a time of cheer, hope, and encouragement. The ISM faculty, researchers, and staff continue to take on
new research challenges, expecting that their academic and social contributions will ripple through the data-driven society of the future, when the
ISM will celebrate its centennial in 2044.
The world today is seeking an ideal form of a data-driven society in whichdata are regarded as valuable assets. The advent of a new IT society is
gradually transforming people’s lives. Statistics sciences help to generateknowledge and value from large bodies of data, expanding the boundaries
of academic arenas and socioeconomic activities.
Past Directors-General of the ISM embraced its mission as an Inter-University Research Institute to create cooperative networks of scientists and academic
institutions that advance data science and its social applications, as well as contribute to academic excellence, help realize a data-driven society, and nurture the next
generation of data scientists. As the 12th Director-General, I will build on those foundations by pursuing the following objectives: (i) Forming a research framework thatallows national and international researchers to advance statistical mathematics
and other statistical sciences, with the goal of extracting effective knowledge andvalue management process from data; (ii) Establishing a platform for discoveringand sharing the standard scenarios in which a wide array of fields, including statistical mathematics and other statistical sciences, are integrated and applied to theprocess of knowledge and value extraction; and (iii) Organizing the support system
for government–industry–academia collaboration to equip next-generation researchers and data scientists for the era of data science.
The ISM aims to deepen the fundamental mathematics for data science, create andactivate research networks, cultivate statisticians, and help visualize their work.
Based on those aims, the ISM is currently promoting, in particular, the Network Of
Excellence (NOE) Project and a pilot version (Phase 1) of the Data Science Faculty
Development Project at the School of Statistical Thinking.
The ongoing global pandemic of Novel Coronavirus Disease 2019 (COVID-19) has
made an unprecedented impact on many aspects of our lives. Despite the Japanese
Government’s declaration of a state of emergency and lockdowns in many othercountries, new cases, hospitalizations of patients in serious condition, and death
tolls have continued to increase. This suggests that our fight against the virus will
be long and uncertain. The ISM is adopting innovative and flexible measures to continue research activities now. I also hope that the outbreak subsides and calm daily
life is restored.
The ISM has a long history of research on statistical modeling of infectious disease,and has developed many experts in its simulation. In this difficult time, the ISM has
launched a research project about preventing various risks related to COVID-19. We
will be happy if we can assist public health experts, epidemiologists, and government
officials who are engaged in tackling COVID-19. We cordially ask for your continued
support and cooperation.

Hiroe Tsubaki

Director-General
The Institute of Statistical Mathematics1Institute Overview

Basic Research
Department of Statistical Modeling
The Department of Statistical Modeling works on structural modeling of physical phenomena related to numerous factors,
and conducts research on model-based statistical inference methodologies. By means of model-based prediction and control,modeling of complex systems, and data assimilation, the department aims to contribute to the development of modeling intel­
ligence in many fields.

■ Prediction and Control Group ————————————————————————————————————————————The Prediction and Control Group works on the development and evaluation of statistical models, which function effectively
in terms of prediction and control of phenomena, decision making, and scientific discoveries. These efforts involve data
analysis and modeling related to phenomena that vary across time and space.

■ Complex System Modeling Group —————————————————————————————————————————The Complex System Modeling Group conducts studies aimed at discovering the structures of complex systems, such as
nonlinear systems and hierarchical networks, through statistical modeling. For these purposes, the group also considers
Monte Carlo simulations, discrete mathematics, and computer science.

■ Data Assimilation Group ———————————————————————————————————————————————The Data Assimilation Group works on the development of data assimilation techniques, which are procedures aimed
at combining information derived from large amounts of observations and a numerical simulation model. By developing
computational algorithms and high-performance parallel computing systems, the group aims to build a next-generation
simulation model that can predict the future in real time.

Department of Statistical Data Science
The Department of Statistical Data Science conducts research on data design methods aimed at managing uncertainty
and incompleteness of information, quantitative methods for evidence-based practice, and related data analysis methods.
Moreover, the department investigates methods for inferring the latent structures in target phenomena from observation data.

■ Survey Science Group ————————————————————————————————————————————————The Survey Science Group promotes research on the design of statistical surveys, development of statistical analysis
methods on survey data, and applications. By exploring complex phenomena in various fields, the group also aims tocontribute to practical applications in academia and policy-making through social surveys.

■ Metric Science Group —————————————————————————————————————————————————The Metric Science Group conducts research aimed at identifying and evaluating statistical evidence through quan­tification of phenomena that have not been measured thus far, as well as efficient information extraction from large
databases. The group investigates related methods and develops methods for analyzing the collected data. By working
on applied research in various fields of real science, the group aims to advance practical, applied, statistical mathematicalresearch based on evidence.

■ Structure Exploration Group —————————————————————————————————————————————The Structure Exploration Group conducts research on statistical science aimed at inferring the latent “structure” behindvarious target phenomena in biology, physics, and social science, based on observational data. The group focuseson machine learning, Bayesian reasoning, experimental design methods, and spatial-temporal analysis methods toinvestigate micro/meso/macroscopic and spatial-temporal dynamic structures in target phenomena.

Department of Statistical Inference and Mathematics
The Department of Statistical Inference and Mathematics carries out research into general statistical theory, statisticallearning theory, optimization, and algorithms for statistical inference.

■ Mathematical Statistics Group ———————————————————————————————————————————The Mathematical Statistics Group is concerned with aspects of statistical inference theory, modeling of uncertain
phenomena, stochastic processes and their application to inference, probability and distribution theory, and the related
mathematics.

■ Learning and Inference Group ————————————————————————————————————————————The Learning and Inference Group develops statistical methodologies to describe the stochastic structure of data math­ematically and clarify the potential and the limitations of the data theoretically.

■ Mathematical Optimization Group —————————————————————————————————————————The Mathematical Optimization Group focuses on mathematical theory and practical applications of optimization andcomputational algorithms together with underlying numerical or functional analysis and discrete mathematics.

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NOE-type Research
Risk Analysis Research Center
Risk Analysis Research Center is pursuing a scientific approach to managing uncertainties and risks in society, which haveincreased with the growing globalization of society and economy. Our research projects are mainly seismology, finance,
resources, environmentology, database development and risk mathematics. The Center also manages a network organization
for risk analysis to facilitate research collaboration across different organizations with the common goal of creating a safe andresilient society.

Research Center for Statistical Machine Learning
Machine learning is a research field associated with autonomous systems that can learn their behavior from data. This field
is based on both the statistical science concerning inference from data and computer science concerning efficient algorithms.
It can be applied to broad disciplines ranging engineering and information science to natural science, such as robotics andbrain sciences. Our research center aims at supporting the academic community of this field, as well as producing influential
research through various joint projects.

Data Science Center for Creative Design and Manufacturing
We aim to foster new scientific methods for innovative design and manufacturing. Various fields in manufacturing are now
facing a revolutionary period. Population reduction and globalization are bringing dramatic changes in the industrial structure
in Japan. Countries around the world has actively developed their growth strategies utilizing data science as a driving force.
We have accumulated state-of-the-art technologies in data science here. We are devoted to foster and practice advanced
methods in data science for design and manufacturing.

Research Center for Medical and Health Data Science
Research Center for Medical and Health Data Science aims to facilitate statistical data science research that covers
medical studies, drug developments, health care, and public health. Our research projects involve fundamental mathematics
and compu­tational science for medical applications, applied methodology for basic, clinical and social medicine, and modern
technology such as artificial intelligence, machine learning, and big data analyses. Furthermore, our research center aims at
constructing a research network of the academic community of this field, as well as offering advanced statistical education
programs.

Professional Development
School of Statistical Thinking
The mission of the School of Statistical Thinking is to plan and implement various programs for statistical thinking, fromextension courses to a professional development program. The researchers affiliated with the school are often involved withspecific data analysis projects, which help them to gain hands-on knowledge of data science. We expect such an experienced
researcher will play an active role as a number-cruncher, as a modeler, or as a project coordinator.

Research Support
Center for Engineering and Technical Support
The Center for Engineering and Technical Support assists academics and their collaborators in many ways: managing
computer systems and networks, editing and publishing journals, maintaining the library, and managing tutorial programs.

■ Computing Facilities Unit The Computing Facilities Unit is in charge of managing computer facilities and scientificsoftware.

■ Computer Networking Unit The Computer Networking Unit is responsible for computer networking and its infra­
structure, and network security.

■ Information Resources Unit The Information Resources Unit is responsible for maintaining a library and an electronic
repository, and is in charge of planning statistical tutorial programs open to the public.

■ Media Development Unit The Media Development Unit is in charge of publishing and editing of research results
and PR brochures.

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Research Topics
Risk Analysis Research Center

The Effort of the Statistical Science towards Solution of Environmental Problems■ Mission of the project
A risk analysis research center / environmental statistics research project aims at performing the contribution towards solution of the environmental problem which is a modern subject by developing the
optimal new statistical methodology for each environmental problem (for example, the problem of dioxin, the problem of global warming, the problem ofcontinuous use of safe water). Moreover, in order torealize this purpose, in cooperation with the community of environmental science, research is carried out
including a visiting teacher or a project researcher.
■ Building an international network ofenvironmental statistics
Since 2013, an international symposium ISM Symposium on Environmental Statistics has been held
every year at the Institute of Statistical Mathematics,
and we have invited key members of The International Environmetrics Society (TIES) as invited speakers. This will contribute to the further developmentof environmental statistics researchers.
We have concluded MOUs with related
foreign universities and established an international research network in this field.

and ozone concentration. An angle has a special feature different from a linear variable in mathematics.
We study statistical modeling and analysis of data
which include angular observations in the framework of directional statistics. We also conduct basic
research on probability distributions that play an important role in analysis in the environmental field.

■ Mercury concentration in environmental media
and its human health risk observed in artisanalvery small gold mining site
The largest source of mercury released into theenvironment on earth is human-powered small-scale
gold mining. Extraction of gold using mercury is popular in developing countries, and there are concerns
about its environmental pollution and its impact onhuman health. In this joint research, we are working
on human health risks from inhaled and oral sources
using actual fieldwork and statistical analysis methods in the field.
Koji Kanefuji■ Statistical approach to risk analysis
It is possible to model the vanishingangles of migratory birds and the orientations of logs on the floor using distributions on the circle. Wind direction is atypical angular variable in environmental
science, and it is important to use information on wind direction for a better understanding of, for example, radiation dose
Photo: ISM Symposium on Environmental Statistics 2019.

Figure : Mercury concentration in environmental media in micro-scale gold mining and smelting area in Bengkulu,
Sumatra, Indonesia.
[ by O. Nagafuchi and K. Nakazawa (Fukuoka Institute of Technology)]4

Statistical Methods for Multivariate Data with Complex Dependence Structure■ Risk analysis and multivariate data
For the management of risks such as financial crises and natural disasters, it is often necessary todeal with multivariate data, namely, data comprisingof multiple variables. For example, the fluctuations ofasset prices of multiple companies can be considered an example of multivariate data. In order tomanage the risks related to the portfolio of such fluctuations, statistical methods for multivariate data
are useful.
■ Multivariate data with complex dependence
structure
Multivariate data observed in practice often havecomplex relationships between variables or, simply,
complex dependence structure. However, many of
the statistical methods proposed in the literature arefor multivariate data with simple dependence structure. Such methods include the ones based on themultivariate normal distributions.
It is known that statistical methods for data withsimple dependence structure could lead to erroneous results if they are fitted to data with complex dependence structure. For example, the fluctuations ofasset prices of a company are often heavy-tailed due
to financial crises such as Black Monday and Subprime Mortgage Crisis. Therefore it is generally not
appropriate to apply the multivariate normal distributions to evaluate the risk of portfolio of such fluctuations (see Figure 1).

■ Statistical analysis using copulas
Copulas are useful statistical tools for analyzing
data with complex dependence structure. Copulas
have gained their popularity in recent
years as a family of probability distributions for the modelling of non-linear and/or asymmetric multivariate
data. Since copulas are distributions
which model only the dependence between variables, the marginal distribution of each variable can be selected independently from the copulas.
Therefore the joint distribution canbe highly flexible and express complex dependence structure. In addition, copulas can be used as scaleFigure 1: Density of: ( left ) bivariate normal and ( right ) skew-Cauchy distributions.
free measures of dependence.
As a joint work related to copulas,
the author has proposed a copula-based measure to compare the upper and lower tail probabilities of bivariate distributions. Here ‘theupper and lower probabilities’ are the probabilities that both variables take extremely large
and small values, respectively. In our work, it
has been seen that the proposed measure hassome tractable properties. Figure 2 displays aplot of the proposed measure applied to dataon the fluctuations of asset prices. The figuresuggests that the values of the proposed measure are small for small values in x-axis, implying that there is asymmetry in tail probabiliFigure 2: Plot of the values (red ) and 90% confidence intervals ( yellow )ties in the sense that the lower tail probabilityof the proposed measure applied to a financial dataset. The
is greater than the upper one.
x-axis represents the values of the tuning parameter of themeasure, while the y-axis denotes the values of the measure.
Shogo Kato
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Research Center for Statistical Machine Learning

Data Science Methods for Astronomy■ Data science and astronomy
Astronomy is one of the oldest sciences. The best
technology of each era was used for observation andnow modern astronomy is trying to utilize advanced
data science methods.
Most of the astronomical observations are detecting electromagnetic waves. Depending on wavelength and each scientific interest, such as nearby
planets, galaxies in distance, black holes of galactic
nuclei, or cosmology, the required information is different. Thus, there are varieties of demands for data
science.
In the last few years, I have been involved in different astronomical projects as a data scientist. In thefollowing, I show two of them.
■ Image of blackhole shadow
On 10 April 2019, in six coordinated press conferences around the world, the first result of the Event
Horizon Telescope (EHT) collaboration was released: The shadow of the supermassive black holeof the elliptical galaxy M87 (Figure 1). The estimate
of its mass is about 6.5 billion solar mass. Although
the size of the hole is about 100 billion km, the apparent size is similar to a tennis ball on the surface of themoon, because it is 55 million light-years far.
EHT is a radio interferometer that combines different radio telescopes. The longest combination was
more than 10,000 km and this was the key to the angular resolution. In order to have an image, interferometers need a computational process called “imaging.” We have developed two imaging platforms for
the EHT. One of them was made by the Japanese

Figure 1: The image of the black hole shadow at the center of thegalaxy M87. Credit: Event Horizon Telescope Collaboration.

6group including me. We have utilized “sparse modeling” for our imaging method.

■ Subaru telescope and type Ia supernovae
Subaru telescope is an optical-infrared telescopeat the summit of Maunakea, Hawai’i. Through thesurvey program, a lot of transient objects have beenfound. Within those transients, type Ia supernovae
are important for cosmology. Since its peak luminosity is consistent, its visual magnitude and redshifttells is the distance and the speed, respectively.
Combining these two, the expansion speed of theuniverse can be estimated.
From the difference of images taken on different
days, a lot of candidates for type Ia are found. But
most of them are bogus. We have developed a system to find good candidates by combining machinelearning methods. With the developed system, we
have found 50 good candidates from a single night
image (Figure 2).
Shiro Ikeda

Figure 2: The difference between the left and the middle is theright. The bottom row is a good candidate for a supernova (Morii et al., PASJ, 68 (6 ), 2016, Fig. 1 ).

Research Topics

Signal Reconstruction by Minimization of Nonconvex Function andNonconvexity Control■ Signal reconstruction in compressed sensing
Compressed sensing is a signal reconstruction
scheme for when the number of measurements is lower than the number of signal dimensions. This problem
setting is underdetermined in general, and the original
signal is not uniquely identified. However, prior knowledge that the signal to be reconstructed is sparse
sometimes allows perfect reconstruction. A widelyused method for signal reconstruction is the ℓ1 minimization method, which corresponds to the minimization problem of the convex function, which is therefore
mathematically tractable. Meanwhile, it is implied thatminimization of nonconvex function is more efficientthan ℓ1 minimization. We introduce the nonconvex
functions Smoothly Clipped Absolute Deviation(SCAD) and Minimax Concave Penalty (MCP) for signal
reconstruction in compressed sensing and analyze
their performances. The nonconvexity of these functions is controlled by two parameters called nonconvexity parameters.

■ Theoretical evaluation of performance andapproximate message passing
We theoretically verified that SCAD and MCP minimization require a smaller number of measurements
than ℓ1 minimization. In particular, as the nonconvexity is enhanced, signal reconstruction is achieved by asmaller number of measurements. Based on this theory, we introduce an approximate message passing(AMP) algorithm for minimization of nonconvex func-

Figure 1: Typical trajectories of AMP for nonconvex function withhigh nonconvexity. Flow to the origin is restricted tothe small ∊ region.

tions. However, AMP sometimes fails in reconstruction, even when perfect reconstruction is theoreticallyguaranteed. What is behind this discrepancy?

■ Failure of reconstruction caused by basin of
attraction shrinkage
In the case of AMP, the typicality of the trajectories
can be described by two macroscopic parameters:
mean squared error between true signal and reconstructed signal (∊) and variance of the reconstructedsignal (V ). When the typical trajectory converges tothe origin, perfect reconstruction is achieved by AMP.
We investigated the trajectories and found that the basin of attraction of the origin shrinks as the nonconvexity increases, and we require initial conditions thatare sufficiently close to true signals for perfect reconstruction.

■ Nonconvexity control
A method to resolve the problem is nonconvexity
control. When the nonconvex function is close to aconvex function, a fixed point, which is not the origin,exists. This fixed point is continuous with respect tothe nonconvexity parameter. Therefore, we start AMP
with a nonconvex function close to a convex function
and approach the origin by slightly increasing the nonconvexity, and finally we reach from the basin of attraction to the origin. We call this method nonconvexity control.
Ayaka Sakata

Figure 2: Trajectories of AMP for nonconvex function with low
nonconvexity. A fixed point (star ) exists.

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Data Science Center for Creative Design and Manufacturing

Data Science for Creative Design and Manufacturing■ Mission statement
We aim to foster new scientific methodologies for innovative design and manufacturing. Various fields in manufacturing are now facing a revolutionary period. Population
reduction and globalization are bringing dramatic changes
in the industrial structure in Japan, resulting in a rapid lossof global predominance in industry. Countries around theworld has actively developed their growth strategies utilizing data science as a driving force, such as the Material
Genome Initiative in the US and the Industry 4.0. Following
the global trend is no longer an effective way to survive in
the intensive power game around the world. In 2017, theInstitute of Statistical Mathematics has established a newresearch center - Data Science Center for Creative Designand Manufacturing. We have accumulated state-of-the-art
technologies in data science here, for instance, machinelearning, Bayesian inference, materials informatics, and soon. We are devoted to foster and practice innovative methods in data science for design and manufacturing throughindustry-academia collaboration.

■ Smart manufacturing
The developments of new materials depend largely on
intuitions of highly experienced professionals, and timeconsuming trial-and-error processes for laboratory synthesis and testing of designed materials based on computer simulation and experiments. On the other hand, recently,
there are new attempts to substitute computational or realexperiments in materials synthesis and testing by statistical models trained by given data. The enormous cost andtime required in the characterization of material structures and physical properties have limited material studies
within a small set of candidates. For example, it has been
proved that high-throughput screening using techniques indata science significantly increases the chance of discovering innovative functional materials. This is a basic conceptof smart manufacturing in the perspective of data science.

■ Creative design and manufacturing
We recognize the importance of being at the absoluteleading edge position in the manufacturing industry. Thiscannot be done by data science alone. Most of the tools indata science are designed for interpolative predictions.
Data science used to be a science of predictions based on
pattern recognition from existing data. For example, we often assume that materials with similar structure exhibit
similar physical properties. However, by definition, newmaterials are unlikely to be similar to any of the existingmaterials. Combination of experiment, theory and data science methods is an essential step to a new breakthrough inthe current state. In other words, we adopt a stepwise approach to expand the region of accurate prediction of a statistical model. We achieve the goal by careful design of an
optimal experiment or simulation schedule for new data
points that efficiently improve an existing predictive model. We have accomplished preliminary success in materialscience using an extrapolating prediction method basedon a data science approach and fostered new collaboration
opportunities between academia and industry for the purpose of new material discovery. Our next step is to extend
the application tovarious fields related to creative design and manufacturing.

Ryo Yoshida

Figure: Discovery of new polymers with high thermal conductivity using machine learning technologies.

8Research Topics

Discovery of New Materials Based on Data Science■ New era of materials informatics
Conventional materials design relies on a trial-and-error
process with expert knowledge to manually propose new
candidates from existing materials and experimentally/
computationally validate the expected material propertiesof the new designs. Such an approach is not efficientenough to handle the rapidly changing market demand andincreasing cost of research and development. Materials informatics (MI), an interdisciplinary field that integrates
data science and machine learning methods to accelerate
the discovery of high performance novel materials, has
emerged to tackle these new challenges. Many national
projects around the world have been developed based on
the concept of MI. In Japan, for example, a Japan Scienceand Technology Agency project called the “Materials research by Information Integration” Initiative (MI2I) were established in 2015 to catalyze the implementation of MI technologies in real world problems. The Data Science Centerof Creative Design and Manufacturing in The Institute of
Statistical Mathematics has been developing many foundational technologies for MI applications and served as aninteractive hub for data scientists and materials scientists.

■ XenonPy: all-in-one open platform for MI
By mapping the highly diverse materials space into a setof “descriptors” and developing a generator of material
candidates, we are able to exploit the existing technologies
in data science to solve the inverse material design problem. To meet the increasing demand of machine learning
tools tailored for various materials sciences applications,our center has developed an all-in-one Python packages,
called XenonPy, for different tasks in materials informatics.
Furthermore, XenonPy provides free access to over 140,000
pretrained predictive models for more than 45 different
properties of organic and inorganic materials.1 This openmodel library, called XenonPy.MDL, combined with othermachine learning modules in XenonPy allows users to customize computational workflows for different material design problems.1■ Extrapolative inverse materials design
XenonPy provides a molecular design algorithm, callediQSPR-X,2 that continuously modifies a set of molecular
structures to new chemically favored structures and picks
out the ones with high probability of exhibiting desired material properties. Our center has succeeded in the discovery of new polymers with high thermal conductivity using
this algorithm.3 Furthermore, we are developing SPACIER,
a machine learning algorithm that possesses extrapolative
power for discovery of truly novel materials. Existing machine learning models are known to be interpolative, i.e.,cannot make accurate prediction in the area without any
data, which is where the novel materials are. Therefore, integration of experiments or computational simulations withdata science is essential to break this bottleneck. SPACIER
uses density functional theory to generate computational
data. Such data is used to update models in iQSPR-X in order to cover a larger search domain, which will eventuallyreach the novel materials. We use SPACIER as a proof ofconcept for successful collaboration between industry andacademia in materials
science.

Stephen Wu
Yoh Noguchi

Figure:An overview of XenonPy’s
basic features and SPACIER
algorithms.
References: 1. Yamada et al. (2019), Predicting materials properties with little data using shotgun transfer learning. ACS Cent. Sci., 5 (10 ):1717-1730.
2. Wu et al. ( 2019), iQSPR in XenonPy: a Bayesian inverse molecular design algorithm. Mol. Inform.
3. Wu et al. ( 2019), Machine-learning-assisted discovery of polymers with high thermal conductivity using a molecular design algorithm.
npj Comput. Mater., 5:66.

9

Research Center for Medical and Health Data Science

Causal Connectivity Analysis of Respiratory Neural Network■ Recording of brain activity by neuroimaging
technique
Recent advances in bioimaging technology havemade it possible to obtain more than tens of thousandsof more accurate spatial information than conventional electrophysiological methods, and to observe spatiotemporal transitions of neuronal activity. However,while the amount of data is enormous, methodologicalstudies to detect neurons related to the event to be investigated and to estimate functional connectivity
among neurons have not been well developed.

but the reliability of the model has not yet been verified by physiological experiments. We estimate thecausal connectivity between neurons that generate
respiratory rhythms in the brainstem and examine themechanism of respiratory rhythm generation fromboth statistical and experimental approaches.

■ Estimation of neuron network by causal analysis

Applying an autoregressive (AR) model that considers only the activity of a certain neuron and an exogenous variable autoregression (ARX) model with inputs
from other neurons, the goodness of fit of the two mod■ Research objectiveels can be evaluated by the Akaike Information Criterion (AIC). When this evaluation method is applied to all
Our group aims to develop a strategy for estimating
combinations of neurons, the significant information
causal connectivity and network structure between
propagation between neurons that generate the respirneurons based on spatiotemporal analysis approachatory rhythm can be estimated as shown in Figure 1 (a).
and experimental verification of that. There have been
In order to assess the estimated neuronal connectivity,
studies on mathematical modeling for this purpose,
we applied an antagonist (CNQX) toblock excitatory signals from excitatory neurons. Figure 1 (b) shows resultsfrom same analysis of the data measured with the addition of CNQX. Since
most of the connectivity has disappeared, it can be said that the possibility of false detection of connectivity between neurons by this analysis method
is low, and that the estimated network
is likely to exist.
By using a multivariate autoregressive (VAR) model, it is possible to examine the timing of information propagation between neurons. When an(a)(b)
excitatory neuron (red arrow) as an inFigure 1: Estimated neuronal network before (a) and after (b) addition of an antagonist
put signal of the optimized VAR, many
(CNQX ).
inhibitory neurons are activated with adelay of about 0.2 seconds (Figure 2).
Currently, we are investigating physiological reasons of the newly appeared
connectivity after addition of CNQX(the orange lines in Figure 1 (b)) anddeveloping a method to quantify thesignificance of neural connectivity. In
the future, we are aiming to develop
methodological studies to detect nonstationary activated neurons and estimate their time-dependent causality.
(a)(b)
Fumikazu Miwakeichi
Figure 2: Impulse responses from an excitatory neuron ( red arrow ) to other neurons(Before ( a ) and after ( b ) addition of an antagonist ( CNQX ) ).

10

Research Topics

Risk Factors for Very Low Birth Weight,
Term-Small-for-Gestational-Age, or Preterm Birth in Japan■ Background about gestational age and birthweight in Japan
From 1985 to 2013, the mean birth weight of infants in Japan decreased from 3120 g to 3000 g, andthe low-birth-weight rate among live births increased
from 6.3% to 9.6%. No prospective study has elucidated the risk factors for poor fetal growth and preterm birth in recent Japanese parents, such as increased parental age, maternal body figure, assisted
reproductive technology (ART), and socioeconomic
status.
■ Statistical analysis for association between
parental characteristics and fetal growth
restriction with DAG
Participants were mother–infant pairs (n = 18,059)
enrolled in a prospective birth cohort in Hokkaido,
Japan from 2002 to 2013. Parental characteristics
were obtained via self-reported questionnaires dur-

Figure 1: DAG for fetal growth restriction.

ing pregnancy. Medical records helped identify preterm-birth (PTB; <37 weeks), very-low-birth-weight(VLBW; <1500 g), and term-small-for-gestational-age(term-SGA) infants. We calculated relative risks(RRs) for PTB, VLBW, and term-SGA birth based onparental characteristics. The relative risks (RR) of
VLBW, term-SGA, and PTB according to parental
characteristics were estimated using multiple Generalized Linear Models. A directed acyclic graph (DAG)
was constructed to identify a minimum set of confounding adjustment (Figure 1). We selected the setof covariates for each factor that were regarded as
the main exposure to effect on RRs of outcome such
as VLBW, term-SGA, and PTB.

■ Results for RRs for PTB, VLBW, and term-SGA
birth based on parental characteristics
The prevalence of PTB, VLBW, and term-SGA was
4.5%, 0.4%, and 6.5%, respectively. Aged parents and
ART were risk factors for PTB and
VLBW (Figure 2). Maternal alcohol
drinking during pregnancy increased
the risk; a parental educational levelof 16 years reduced risk of term-SGA.
Maternal pre-pregnancy BMI of <18.5kg/m2 increased the risk of PTB andterm-SGA. The RR for low BMI was
highest among mothers who have low
educational level. Among various factors, appropriate nutritional education to maintain normal BMI is important to prevent PTB and term-SGA in
Japan.
This is collaborative study with
Hokkaido Study of Environment andChildren’s Health conducted by Center for Environmental and Health Sciences, Hokkaido University.
Naomi Tamura

Figure 2: Significantly relative risks for fetal growth restriction with parental characteristics.

Reference:
Naomi Tamura, et al. Parental risk factors for infants being preterm, very low birth weight, or termsmall for gestational age in Japan. International
Journal of Environmental Research and Public
Health 2018, 15 (2 ), 369.

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Research Topics
Next-Generation Simulation NOE

Estimation of Animal Behavior■ Movement modeling of animals
Understanding of spatial utilization of animals is essential to promote biodiversity conservation, animal
damage control, and dispersion control of invasive species effectively. Each species has species-specific environmental preferences (i.e. ecological niche). Based on
this premise, the probabilities of species distribution
and abundance have been estimated using species-environment relationships (i.e. habitat modeling: Figure 1).
However, animals usually exhibit high spatial mobility,
and, therefore, it is also required to grasp their dynamic
movement between habitats for efficient conservationand management initiatives. Recently, small animalborne data loggers have been widely applied to record
behavior of animals (called Bio-Logging). On the other
hand, while current status of given species can be examined using this method, predicting how environmental
alterations caused by climate change and environmental
degradation affect them is still obscured. So, to estimate
movement of animals, I try to develop a movement model by incorporating their behavioral transition probabili-ties in relation to environmental stimuli (e.g. stay, evasion, inversion), optimized by data assimilation method
using GPS tracking data.

■ Understanding the human-wildlife conflict
Recently, we published new study, showing the human-wildlife conflict by estimating seasonal movementof animals. Magellanic penguins breed along the coasts
of Chile and Argentina, and they are listed as nearly endangered. The population of this species has been declined, which is presumed to relate to high mortality offemales during non-breeding season. However, their behavior during this period has been unknown. So, in collaboration with CONICET (National Scientific and Technical Research Council, Argentina), we examined seasonal
movement of penguins during the non-breeding periodusing a data logger. In this study, we used a data loggerthat can record light intensities over a year, and estimated probable positions based on day length and the timing of local noon with respect to GMT. As a result, wefound that at-sea distributions differed between sexes,
and wintering area of females wasoverlapped with areas of man-madethreats, including oil development,
marine transport, and fishery-associated hazards (Figure 2). This research
was released in EurekAlert! officiallyby the published journal (Current Biology) and received attention worldwide.

■ New approach for animal
behavior monitoring
Figure 1: Overview of habitat modeling, which estimate probable distribution andabundance of animals by species-environment relationships ( Yamamoto etal. 2015 Ecological Applications).

Figure 2: An example of probable trajectory estimated using light intensity data ( left )and at-sea distribution of male and female penguins during the non-breeding period ( right ). Females occupied areas with man-made threats ( Yamamoto et al. 2019 Current Biology).

12

Behavioral observation is one ofimportant methods to assess an animal’s quality of life, reared at farms
and zoos. However, long-term and continuous observation of animals is noteasy to do, and also number of individuals observed is limited. In collaboration with zoos and aquariums in Japan,
I have been recording acceleration ofanimals using a data logger, and applied time-series analysis (e.g. hidden
Markov model) to estimate hidden
state (i.e. behavior) to establish low
time- and effort-consuming monitoring
approach.

Takashi Yamamoto

Research Topics
Survey Science NOE

Visitor Studies in Science Communication for Developing Survey Methodologies■ Institutional public communication of science
Ministry of Education, Culture, Sports, Science,
and Technology has recommended the promotion ofscience communication activities since its Third Science and Technology Basic Plan. The majority of universities and research institutions have responded
by holding events such as open houses to facilitate
active dissemination of scientific information. The
question remains as to how the opportunity to receive such scientific experience is distributed among
the citizens1. Those who do not take the opportunities offered will not receive them. Who are the actual
citizens attending open house events at scientific research institutions, and how they are distinctivefrom general Japanese public?

is an effective approach of answering the above
question. We found that as compared to the general
Japanese public, participants of science communication activities believed highly in the value of scientific research and have greater amount of cultural
capital not only science and technology but also literature and art. However, we did not observe any
statistical differences with regard to their assessment of the level of science, art, or the economy inJapan.2■ Elaboration of survey methodologies withvisitor studies research
Visitor surveys have many potentials for contributing development of survey methodologies. This
study uses different measurement methods of visi■ Statistical comparison of visitor surveys andtors’ exhibit-viewing behaviors such as questionnationally representative surveys
naires and smart card records to examine the reliability and validity of the measurements (Figure 1).2
The statistical comparison of visitor surveys of
Because the purpose of visitor surveys was not althe open houses and nationally representative surways exclusively research use, they may containveys including Japanese National Character Survey
noise in the measurements of exhibit viewing behaviors. For example, visitors were incentivized touse smart cards to participate in abingo game (Figure 2) and such incentives may influence measurements of total exhibit-viewing time
and total number of exhibits viewed.
Further utilizing such drawbacks
including effects of noise, we aim
to contribute to elaboration of survey methodologies by combining
Figure 1:
multiple methods of measurement
The smart card (Felica sysFigure 2: Touching the electric card at a checkpoint attem) used to measure ex(Table 1).
hibit-viewing behaviors.
participating in an exhibit.
Naoko Kato-Nitta

Table 1. Estimating mixed distributions using a normal mixture model: There was a groupof people who stopped touching their smart cards halfway through even though
they claimed to have viewed more of the exhibits in the questionnaire.

References:
1. Kato-Nitta, N. (2013). Public Understandingof Science, 22(3 ), 321-334.
2. Kato-Nitta, N., Maeda, T., Iwahashi, K., & Tachikawa, M. ( 2018). Public Understanding of
Science, 27( 7 ), 857-875.

13

NOE ( Network Of Excellence ) Project
Building a Framework for Advancing Strategic Research andPursuing a New Approach to Collaborative Research■ Research and Educational Activities asa Biaxial Structure

The Institute of Statistical Mathematics (ISM) pursues
research and education in the own biaxial structure; oneis structured by three basic research departments andthe other is by four NOE-research centers and one schoolfor professional development. By its nature, the basic research departments cut across and link various disciplines, with the goal of developing tools for interdisciplinary research. The field of statistical mathematics must
itself evolve to meet the changing needs of society andthe data environment, and is therefore constantly evolving as a field of study. At the same time, there are approaches and directions that have remained unchanged
as the field evolves. There are three basic research departments: Statistical Modeling, Statistical Data Science,and Statistical Inference and Mathematics. All tenured
research staff in ISM are assigned in principle to one ofthese basic research departments. These departments
engage in cutting edge research aimed at developing
methodologies for rational prediction and decision making, based on data and existing knowledge. We regularly
assess whether our research system is functioning effectively from the viewpoints of research trends and prospects in statistical mathematics.
On the other hand, the NOE-type research centers andthe school (along the vertical axis) are staffed by permanent researchers within ISM, project professors/researchers (post-doctoral researchers), and visiting professors
and researchers. There are four NOE-type research cen-Fig. 1: Brief history of the NOE ( Network Of Excellence ) Project.

14

ters: the Risk Analysis Research Center (RARC), the Research Center for Statistical Machine Learning, the Data
Science Center for Creative Design and Manufacturing(DSCCDM), and the Research Center for Medical and
Health Data Science (RCMHDS). These centers conduct
research activities that interface statistical mathematics
with individual scientific disciplines in order to find solutions to urgent social problems. And the School of Statistical Thinking is for professional development. Pleaserefer to the page “Project of Fostering and Promoting
Statistical Thinking” for more details.

■ History of the NOE ProjectIn accordance with the second medium-term plan for
Research Organization of Information and Systems (ROIS,
ISM’s parent organization), ISM had set as a goal the establishment of NOEs (Networks Of Excellence) in statistical mathematics. Initially, ISM established NOEs in Risk
Research, Next-Generation Simulation, Survey Science,
Statistical Machine Learning, and Service Science. Overthe next few years, however, ISM reorganized the domains of NOEs or NOE-type Research Centers considering the needs of each community as well as modern society as a whole. We closed the Survey Science Center in
FY 2016. Its projects were transferred to one of the centers in the Joint Support Center for Data Science (DS) of
ROIS. Survey Science Group at the Dept. of Statistical
Data Science, which is now the core organization, is responsible for connecting and establishing the Survey
Science NOE. The Service Science Research Center was

ISM Projects

also closed in FY 2016, and its projects were distributed
to other Centers according to their methodologies. In
July 2017, ISM launched DSCCDM, and also establishedthe Data Science for Creative Design and Manufacturing
NOE, which inherited a part of the Service Science NOE.
RCMHDS was set up in April 2018. The Center, which waslaunched from one of the projects of RARC, is promoting
research and educational activities in medical healthdata science domains based on the knowledge and networks that ISM obtained in statistical mathematics both
inside and outside Japan (Fig. 1). The Research and Development Center for Data Assimilation was also closed
at the end of FY 2018. Each project has been transferred
to another center in DS, like the Survey Science Center.
The Data Assimilation Group at the Dept. of Statistical
Modeling in ISM has inherited the establishment of the
Next-Generation Simulation NOE. This April, we formally
set up the new NOE domain and its research unit, Data
Science for Resource-Environment Management. As ofthis FY, ISM is promoting projects with seven NOE domains: Risk Research, Next-Generation Simulation, Survey Science, Statistical Machine Learning, Data Science
for Creative Design and Manufacturing, Medical and
Health Data Science, and Data Science for Resource-Environment Management (Fig. 2).

■ Future Perspective of NOE Activities
This NOE Project is a core theme of ISM. To fulfill thegoal of establishing new scientific methodologies in aknowledge-based society, in which the importance ofknowledge goes beyond merely solving individual problems, the NOE activities are being systematically pursued under the unified guidelines formulated by the Managing Committee of the NOE Project. We also commission
experts from the industrial, academic, and government

sectors to be members of the Advisory Board of the NOE
Project, and their advice helps us to promote the projectmuch more effectively. The 3rd Advisory Board Meetingof the NOE Project was held on December 17, 2019. All six
members of the board in each research domain attended
the meeting and engaged in discussion with core members of the project at ISM.
On the basis of this project, ISM, as an Inter-University
Research Institute, will be providing the industrial, academic, and government communities with further opportunities for joint usage (of facilities) and joint research.
This project, made possible by ISM’s special focus in the
cross-disciplinary field of statistical mathematics, is attracting strong support from each of these communities.
As described above, each NOE-type research center
serves as a core hub in its respective fields. The numberof ISM’s Memoranda of Understanding with research organizations within Japan and overseas is increasing
each year, including many that span multiple research
fields. ISM’s general research in statistical mathematics,
which is in demand by various research fields in both thehumanities and sciences, could connect existing fields
and create new research fields. Hence, ISM plans for the
NOEs to become broader, not only in statistical mathematics but also in data science. ISM is continuously expanding and developing this NOE project with the aim ofestablishing new scientific methodologies (“Fourth Paradigm”), creating new research disciplines, and developing new styles of joint research. For up-to-date information, please visit the website. We truly appreciate yourunderstanding and support for this project.

https://www.ism.ac.jp/noe/project/en/

Fig. 2:
Relationship diagramof the NOE ( Network
Of Excellence) Project.

15

Project for Fostering and Promoting Statistical Thinking

Rapid development of information and communication technology has led to the explosion of data. Now surrounded by “Big Data”, everybody is expected to “think statistically”. More than ever, there is a need for data
scientists who can handle such big data and are able to extract useful knowledge from it. Meanwhile, Japanesehigher education is exhibiting a deplorable lack of production capacity in terms of data scientists. This can be accounted for by the fact that, until quite recently, no academic institution other than ISM had a Ph. D. course in statistics and the small number of statisticians in academia are isolated from each other, being scattered over various disciplines. Hoping to gain a little traction on this problem, ISM established the School of Statistical Thinking,into which we integrated all of our educational resources. In FY 2016, ISM established the Managing Committeeof School of Statistical Thinking, inviting contributions from outside experts, and in FY 2017 we launched the
Leading DAT program by adopting the suggestions by the committee. The following are the principal projects.

Research Collaboration Startup
The Institute had already been providing a consultation service for statistical science, but along withthe launch of the School of Statistical Thinking in November 2011, this service was reorganized as a research collaboration startup. This program, being
one of the projects to foster and promote statisticalthinking, is mainly aimed at supporting applied scientists and other non-experts. Expert statisticians
affiliated with the Institute give them advice on statistical modeling, data analysis, and research. Somecases have developed into official research collaborations, which are our primary duty as an inter-university research institute. The Institute accepts morethan 30 cases annually, some of which benefit society in diverse ways.

Data Science Research Plaza
Researchers funded by private-sector firms canmaintain a desk and phone in the School of Statistical Thinking. This program is subject to fees, and thecontract can be renewed annually. A faculty mentor
gives advice to the accepted funded researcher so

Statistical Mathematics
Seminar Series
The Institute holds weekly seminar series on statistical mathematics every Wednesday. The seminars
are led by in-house and external lecturers to showcase their latest studies. These lectures are free toattend. To view the seminar schedule and learn more
about the program, please visit the Institute of Statistical Mathematics website.
https://www.ism.ac.jp/index_e.html
16that he or she can freely attend various events, suchas seminars, workshops, conferences, and extension
courses. After learning the expertise of the ISM research staff, participants in this program are invited
to take advantage of paid consultations and funded
research collaboration.

ISM Projects

Open-type Professional Development ProgramThis is a spin-out program from ISM cooperative
research projects. Establishing a goal is an indispensable element of the proposal of a cooperative research project. On the other hand, such goal setting
is irrelevant for a summer school program, study
session, or retreat. Since the launch of the School of
Statistical Thinking, organizers of such group-orient-

ed study programs can apply to the Open-type Professional Development Program. There are two categories under this program: one is ‘workshop’ andthe other is ‘intensive training for young researchers’. For FY 2019, five workshops have been accepted
after review.

Leading DATIn FY 2017, the School of Statistical Thinking
launch­ed a program called “Leading DAT” aimed attraining data scientists with the knowledge and skills
in statistical mathematics required by modern society. In FY 2019, we organized three Leading DAT lectures entitled “L-A Introductory Data Science”, “L-B1
An Introduction to Statistical Modeling” and “L-B2
Machine Learning and Modern Methodologies in Data

Science”. Around half of the attendants of L-B1 andL-B2 enroll the Leading DAT Training Course, in which
we grant certificates to participants who have fulfilled the course requirements, including attendancein L-B1 and L-B2 lectures and submission of reports.
A total of 44 people have been granted the certificate
of completion.

Group photo of attendees to the Leading DAT Training Course

Tutorial Courses
The education program at ISM dates back to 1944,
the year of founding. The Ministry of Education installed a training center within ISM to foster technicians in numerical computation. After the World War
II, this training center was relaunched in 1947 to develop pollsters and census takers. It helped to cultivate professionals in the field of statistical surveys,
while a growing number of entries from business andindustry coming for various types of training werealso observed.
Now the tutorial courses are operated by the

School of Statistical Thinking, which was establishedin 2011.
In FY 2019, 9 lectures and one course (including
Leading DAT) were held and the number of participants was 878. The total numbers of lectures andcourses held from 1969 to March, 2020 were 382 and3 respectively, with a total of 27,899 participants.
These lectures covered a wide range of fields frombasic to applied statistics. The schedule of tutorial
courses can be found on the website of the Instituteof Statistical Mathe­matics.
https://www.ism.ac.jp/lectures/kouza.html
17

Research Cooperation

International Cooperation■ Associated Foreign Research Institutes
Organization name

Address

Conclusion day

The Statistical Research Division of the U.S. Bureau of the Census
Stichting Mathematisch Centrum
Institute for Statistics and Econometrics, Humboldt University of Berlin
The Steklov Mathematical InstituteCentral South University
Soongsil University
University of Warwick
Indian Statistical Institute
Institute of Statistical Science, Academia Sinica
Department of Empirical Inference,
Max Planck Institute for Biological Cybernetics
Department of Communication Systems,
SINTEF Information and Communication Technology
University College London
Department of Electronics and Telecommunications,
Norwegian University of Science and Technology
Department of Probability and Mathematical Statistics,
Charles University in Prague
Department of Ecoinformatics, Biometrics and Forest Growth of the
Georg-August University of Goettingen
Korean Statistical Society (KSS )USA ( Washington )July 27, 1988

Germany ( Berlin )December 8, 2004

Russia ( Moscow )August 9, 2005

China ( Changsha )November 18, 2005

The Republic of Korea ( Seoul )April 27, 2006

The United Kingdom ( Coventry )January 16, 2007

India ( Kolkata )October 11, 2007

Taiwan ( Taipei )June 19, 2008

Germany ( Tubingen )August 11, 2010

Norway ( Trondheim )January 30, 2012

The United Kingdom ( London )February 16, 2012

Norway ( Trondheim )May 22, 2012

Czech Republic (Prague )October 10, 2012

Germany (Goettingen )October 18, 2012

The Republic of Korea ( Seoul )July 9, 2013

Toyota Technological Institute at Chicago
Australian National University
RiskLab ETH Zurich
Institut de Recherche en Composants logiciel et matériel pourl’Information et la Communication Avancee
Le laboratoire de mathématiques de I’Universite Blaise Pascal
Centre de Rechereche en Informatique, Signal et Automatique de Lille
University College London Big Data Institute
The Institute of Forestry, Pokhara of Tribhuvan University
The Institute of Forest and Wildlife Research and Developmentof the Forestry Administration of Cambodia
The Chancellor masters and Scholars of the University of Oxford
Forest Inventory and Planning Institute of Vietnam
Zuse Institute Berlin
The University of Porto
National University of Laos
Institute of Geophysics China Earthquake Administration
Hong Kong Baptist University
University of Malaya
Unversidade de Évola
Universität Ulm
The Korean Association for Survey Research
The Jean Golding Institute for data-intensive research, University of Bristol
Survey Research Center, Sungkyunkwan University
University of Lampung
Department of Earth and Space Sciences, Southern University of
Science and Technology
Université Bretagne Sud
North Carolina State University

USA ( Chicago )February 10, 2014

Australia ( Canberra )May 15, 2014

Switzerland ( Zurich )February 7, 2015

France ( Paris )February 9, 2015

France (Clermont-Ferrand )February 11, 2015

France ( Paris )February 12, 2015

The United Kingdom ( London )February 26, 2015

Nepal ( Pokhara )March 6, 2015

Cambodia ( Phnom Penh )March 6, 2015

The United Kingdom ( Oxford )March 10, 2015

Vietnam ( Hanoi )June 2, 2015

Germany (Berlin )June 20, 2016

Portugal ( Porto )June 22, 2016

Laos ( Vientiane )March 15, 2017

China ( Beijing )April 28, 2017

Hong Kong ( Kowloon Tong )August 28, 2017

Malaysia ( Kuala Lumpur)

September 18, 2017

Portugal ( Evola )November 30, 2017

Germany ( Ulm )December 8, 2017

The Republic of Korea ( Seoul )February 14, 2018

The United Kingdom ( Bristol)January 15, 2019

The Republic of Korea ( Seoul )February 25, 2019

Indonesia ( Lampung )March 6, 2019

China ( Shenzhen Shi )March 25, 2019

France ( Lorient )March 29, 2019

USA ( Raleigh )November 13, 2019

National University of Singapore
Konrad-Zuse-Zentrum für Informationstechnik Berlin

Singapore
Germany ( Berlin)

February 25, 2020

Singapore-ETH Centre

Singapore

March 18, 2020

* There are two more agreements concluded.

18

The Kingdom of the Netherlands ( Amsterdam ) May 10, 1989

Research Collaboration
ISM performs many activities for collaborating with researchers in the various fields of statistical science,from the individual level to the national level. The ISM cooperative research program regularly performs research activities to provide the research resources of ISM to researchers at universities or research institutes inorder to advance their academic research. Available research resources include books, journals, supercomputers, some commercial statistical software packages, as well as statistical packages developed by ISM, and also
the researchers in ISM themselves, who have abundant professional knowledge and experience in statisticalscience and data analysis. The ISM cooperative research program provides not only research support funds
but also opportunities for the various researchers in many fields who require statistical knowledge to make useof the resources available at ISM. ISM’s aim is to be a place for interaction and fusion among researchers insideand outside of ISM, and to contribute to multidisciplinary development of both the theory and the applicationof statistical science.

■ Number of Activities
Year

2014

2015

2016

2017

2018

2019

Number of Activities

177

183

187

161

166

178

■ Fields of Research Collaboration
Research collaboration is classified by research field as follows. Applicants can use the table below to findthe most appropriate type of project.
ISM Fields
NumberFieldsNumberFieldsa

Prediction and Control Groupf

Structure Exploration Groupb

Complex System Modeling Groupg

Mathematical Statistics Groupc

Data Assimilation Grouph

Learning and Inference Groupd

Survey Science Groupi

Mathematical Optimization Groupe

Metric Science Groupj

OthersMajor Research Fields
NumberFieldsMajor Research Domains

1

Statistical mathematics

Mathematical theory of statistics, optimization, etc.

2Information science

Algorithms, use of computer in statistics, etc.

3

Biological science

Medicine, pharmacy, epidemiology, genetics, etc.

4Physical science

Space, planet, earth, polar region, materials, etc.

5Engineering

Mechanics, electronics, control, chemistry, architecture, etc.

6

Human science

Philosophy, art, psychology, education, history, geography, culture, language, etc.

7Social science

Economics, law, politics, society, manage­ment, official statistics, population, etc.

8

Environmental science

Environmental statistics, environmentrics, agricultural statistics, statistical meteorology,
land economics, landscape management, forest management, etc.

9

OthersOther research fields

19Graduate School Program

Organization
The Institute of Statistical Mathematics is one of the platforms of SOKENDAI (The Graduate University for
Advanced Studies; the headquarters in Hayama, Kanagawa), which was opened in October 1988 to offer graduate education. Since its opening, the Institute has included the Department of Statistical Science and, sinceApril 1989, has accepted students for education and research in doctoral programs. In 2006, the Instituteadopted a five-year system, offering either a five-year education and research program, or a three-year education and research program starting from the third year of study.

Outline of Education and Research
The Department of Statistical Science, which is based on the Institute of Statistical Mathematics (ISM) serving
as its underlying platform, aims to cultivate individuals who possess creative research skills to contribute tosolving various important intricately-intertwined problems. To this end, the Department conducts education
and research related to the basis, mathematics and applications of data collection designs, modeling, inference
and prediction, and equip students with the ability to extract information and knowledge from the real world
based on the effective use of data.
Field of Education and Research

20

Contents

Statistical Modeling

Education and research focuses on dynamic modeling such as spatial and space-time modeling, graphicalmodeling of temporally and/or spatially inter-related complex phenomena, and intelligent information
processing. We also provide education and research on statistical inference based on various models,
methods of calculation for inference, and evaluation of models based on data.

Statistical Data Science

We provide education and research on data design, investigation and analytical methods to cope with theuncertainty and incompleteness of information, as well as on computational statistics.

Statistical Inference
and Mathematics

We provide education and research on the theory of statistics and related basic mathematics, statistical learningtheory to extract information from data by automated learning and inference techniques; as well as theory andapplications of optimization and computation algorithms which serve as the basis for computational inference.

Features of Education and Research● The course is the only integrated doctoral program on statistical science in Japan. It has received students

from a wide variety of disciplines and has offered education and research on statistical science by professors
specialized in many different fields, from theory through to practical applications.

● The Institute of Statistical Mathematics, the platform for the course, is equipped with a world-class supercomputer, high-speed 3D graphic computers and simulators to generate physical random numbers, as well
as a variety of software, including original statistical software developed by the Institute.

● The academic publications and books on statistical and mathematical sciences produced are some of the bestin the world.

● In its role as an inter-university research institute, the Institute holds frequent workshops and seminars byvisiting professors and researchers from both Japan and abroad. Students are free to attend and participate.

● It is possible to collaborate with researchers from other universities and institutions. It is also possible forstudents to develop their own projects by participating in research projects with other institutions.

Course Requirements and Type of Degree Granted● Requirements to complete the doctoral course are as follows:

Completion of at least 40 credits, including the required ones, by a student in the five-year program, or completion of at least 10 credits by a three-year doctorate student who previously completed a Master’s course;
meeting all the criteria set by the thesis committee of the Institute; and successfully completing the examination.

● On completion of the course, either a Doctorate in Statistical Science or, if the thesis deals mainly with aninter-disciplinary field related to statistical science, a Doctorate of Philosophy is awarded.

● The required number of years of study will be flexible if a student demonstrates outstanding research results.

Number of Students ( As of April 1, 2020)■ 5-year doctoral

course:Quota,2■ 3-year doctoral

course:Quota,3Year of enrollment

2013

2016

2017

2018

2019

2020

Number of students

1

1

1

1

2

0

Year of enrollment

2015

2016

2017

2018

2019

2020

Number of students

1①

4③

3③

7⑤

15 ⑫

3①

* The figures in circles indicate those who are employed by other organizations.

University Background of Students
National and public universities• Hokkaido University ( 5 ) • Tohoku University (4 ) • Fukushima University (1) • University of Tsukuba (7 ) • Saitama University (1)• Chiba University (1) • Ochanomizu University (1) • National Graduate Institute for Policy Studies (1) • Tokyo Medical and Dental
University (1) • Tokyo University of Marine Science and Technology (1) • Tokyo Gakugei University (2) •Tokyo Institute of Technology ( 6 )• The University of Tokyo (22 ) • Tokyo Metropolitan University (1) • Tokyo University of Agriculture and Technology (1) • Hitotsubashi
University (6 ) • Shizuoka University (1) • Kanazawa University (1) • Japan Advanced Institute of Science and Technology (1) • Nagoya
University (4 ) • Toyohashi University of Technology ( 2 ) • Kyoto University ( 7 ) • Osaka City University (1) • Osaka University ( 3 )• Nara Institute of Science and Technology (1) • Okayama University ( 2 ) • Shimane University (3 ) • Kyushu University ( 3 ) • Oita
University (1)21Graduate School Program

University Background of Students
Private universities• Aoyama Gakuin University (1 ) • Kitasato University (1 ) • Keio University (8 ) • International Christian University (1) • Shibaura Institute ofTechnology (1 ) • Sophia University (1 ) • Chuo University (9 ) • Tokyo University of Science (7 ) • Toyo University (1) • Japan Women’s
University (1 ) • Nihon University ( 2 ) • Hosei University (7 ) • Waseda University (9 ) • Nanzan University (1 ) • Osaka Electro-Communication
University ( 1 ) • Kansai University ( 1 ) • Kyoto Sangyo University (1 ) • Ritsumeikan University (1 ) • Okayama University of Science (1 )• Kurume University (1 )Foreign universities• Aston University (1) • University of California, Irvine (1) • California State University, Long Beach (1) • University of Campinas (1)• University of Colorado Boulder (2) • University of Dhaka (2 ) • University of Hawaii (1) • Jahangirnagar University (2 ) • University of
Malaya (1) • Northeast Normal University (1) • Ohio University ( 2) • University of Rajshahi (2 ) • Stanford University (1) • The University of
Nottingham (1 ) • Zhejiang University (1 ) • Institute of Applied Mathematics, AMSS, CAS (1 ) • University of Science and Technology ofChina (1 ) • Center for Analysis and Prediction, China Seismological Bureau (1 ) • Northeastern University (1 ) • The Hong Kong Universityof Science and Technology (1)Degrees AwardedYear
Doctor of Philosophy

2013

2014

2015

2016

2017

2018

2019

6

5

5

7

5

5

5

AlumniNational and public universities, and public organizations• Obihiro University of Agriculture and Veterinary Medicine • University of Tsukuba • University of Hyogo • The University of Tokyo• The University of Electro-Communications • Saitama University • Nagoya University • Kyushu University • Kyushu Institute of Technology• University of the Ryukyus • The Institute of Statistical Mathematics • Tohoku University • Yokohama National University • Hokkaido
University • Tokyo Institute of Technology • Hiroshima University • Oita University of Nursing and Health Sciences • JAXA’s Engineering
Digital Innovation Center • Kyoto University • Nara Institute of Science and Technology • Bank of Japan • Japan Broadcasting Corporation• Railway Technical Research Institute • Statistical Information Institute for Consulting and Analysis • Government Pension Investment Fund• Public School • RIKEN • Statistics Bureau of Japan • Pharmaceuticals and Medical Devices Agency (PMDA)
Private universities• Sapporo Gakuin University • Tokyo Health Care University • Meiji University • Doshisha University • Josai University • Nihon University• Komazawa University • Aichi University of Technology • Tokyo University of Information Sciences • Shibaura Institute of Technology• Rikkyo University • Waseda University • Keio University
Foreign universities• Jahangirnagar University • Victoria University • Massey University • University of Otaga • Statistics New Zealand • University of
Rajshahi • University of California, Los Angeles • Asia-Pacific Center for Security Studies Department • Central South University • Hong
Kong Baptist University • University of South Carolina • The University of Warwick
Private companies, etc.
• Hitachi, Ltd. Central Research Laboratory • NTT Communication Science Laboratories • Seiwa Kikaku • NLI Research Institute • Mizuho
Trust and Banking • Nomura Securities Co., Ltd. • ATR Computational Neuroscience Laboratories • Toyota Motor Corporation, Higashi-Fuji
Technical Center • Schlumberger Limited • Macquarie Securities, Japan • Non-Life Insurance Rating Organization of Japan • Barclays
Global Investors • Open Technologies Corporation • Yamaha Corporation • Goldman Sachs Asset Management L.P. • CLC bio Japan, Inc.
• MUFG Bank, Ltd. • Pfizer Japan Inc. • Doctoral Institute for Evidence Based Policy • Sony Corporation • NTTIT Corporation • Sompo
Japan Insurance Inc. • Qualicaps Co.,Ltd. • Bridgestone Corporation • Brain Pad Inc. • Sumitomo Chemical Co.,Ltd.
• PricewaterhouseCoopers Aarata • Mitsubishi Tanabe Pharma Corporation • Daiichi Sankyo Co.,Ltd. • Shizuoka Cancer Center • CPC
Clinical Trial Hospital, Medipolis Medical Research Institute • CRD Association • Japan Society for the Promotion of Science • Tokyo
Electric Power Company Holdings, Inc. • Asahi Kasei Corporation • Honda R&D Co.,Ltd. • Yokogawa Electric Corporation • Kao
Corporation • Advanced Smart Mobility Co., Ltd. • NEC Corporation • Janssen Pharmaceutical K.K.

22

Facilities and Equipment

Computational Resources ( As of April 1, 2020 )ISM is operating a supercomputer system, the Supercomputer System for Statistical Science, which is suitable for analyzing large-scale datasets. The Supercomputer System for Statistical Science, an HPE SGI 8600 system,has been operated since October 2018. The system is a
distributed-memory parallel computer that has total theoretical peak performance of 1.49 petaflops. The system
is liquid cooled and consists of 384 compute nodes. Eachnode has two CPU chips (Intel Xeon GOLD 6154) with 18
cores and has 384 GB memory. The system includes hardware random number generator. For visual representation of research results, ISM has a 200-inch wide screen
and a projector capable of showing 3D movies in 4K resolution in the historical computers exhibit room.

In the main office building, the primary local area
network (LAN) consists of an Ethernet network using
10GBase-SR for the main trunk and 1000Base-T for branches. The personal computers in researchers’ offices and thesupercomputer system are all connected to this network.
A wireless LAN system is also available in the immediatearea of the building occupied by ISM. These LAN systems
enable distributed processing and allow computing resources and statistical data to be used effectively. Comprehensive network security methods have been implemented, such as a firewall system, anti-virus software,and an intrusion prevention system. To encourage joint
research with researchers both in Japan and abroad, as
well as the exchange of e-mails, the network is connectedto the Internet through SINET5 (40 Gbps).

Supercomputer System for Statistical Science ( HPE SGI 8600 )

200-inch wide screen showing 3D movies in 4K resolution

Library and Materials ( As of April 1, 2020 )We have a large number of major Japanese/foreign
journals covering a wide variety of fields including statistics, mathematics, computer science and informatics. In
addition, we also have a large library consisting of books
on humanities, social science, biology, medical science,science and engineering.
Besides contributed to Japanese and foreign pub­lications, we also have a collection of journals that wepublish ourselves: Annals of the Institute of Sta­tistical
Mathematics (English; Springer), Proceedings of the Institute of Statistical Mathematics (Japanese), ISM Survey
Research Report (Statistical Researches mainly related
to the Japanese National Character), Computer Science
Monographs, Cooperative Research Re­ports (for collaborative research projects), Research Memorandum, ISM

Reports on Statistical Computing, and ISM Report on
Research and Education.
All materials are properly catalogued and can besearched from the web in order to meet the needs ofresearch­ers working in a wide of fields. We also accept
photocopy requests.

23

Finance and Buildings

Administration Subsidy and Others ( 2019 )Type

Personnel expenses

Non-personnel expenses

Total

664,233

921,088

1,585,321

Expenditure

Unit: 1,000JPY

Accepted External Funds ( 2019 )
Type

Joint
research

Subcontracted
research,
Trustee businessAcademic
Consulting

Contract
researchers

Contribution for
scholarship

Total

Items

24

26

7

1

8

66

Income55,273

134,900

5,815

279

9,390

205,657
Unit: 1,000JPY

Grant-in-Aid for Scientific Research “ KAKENHI” ( 2019 )Research Category

Items

Amount Granted

Grant-in-Aid for Scientific Research on Innovation Areas

1

14,820Grant-in-Aid for Scientific Research ( S )——Grant-in-Aid for Scientific Research ( A)

650,960Grant-in-Aid for Scientific Research ( B)7

32,110Grant-in-Aid for Scientific Research ( C)22

26,910Grant-in-Aid for Challenging Research ( Exploratory )

3

4,680

Grant-in-Aid for Young Scientists ( B )

5

6,554

Grant-in-Aid for Early-Career Scientists

8

9,490

Grant-in-Aid for Research Activity Start-up

1

1,430

Grant-in-Aid for JSPS Fellows

1

910

Grant-in-Aid for Publication of Scientific Research Results

1

789

55

148,652

Total

Unit: 1,000JPY

Site and Buildings ( As of April 1, 2020 )24

Site Area

62,450m 2

Area for Buildings (total)

16,209m 2

Organization

Organization Diagram ( As of April 1, 2020 )25

Number of Staff ( As of April 1, 2020 )DirectorGeneral

Type

Director-General

Assistant Administ­ Technical
Professor Associate
Staff
Professor Professor rative Staff

Total

1

1

Department of Statistical Modeling

7

5

1

13

Department of Statistical Data Science

4

7

2

13

Department of Statistical Inference and Mathematics

7

8

15

School of Statistical Thinking

0Center for Engineering and Technical Support
Administration Planning and Coordination Section

10

( 35)

(1 )

( 36 )

1 ( 35)10 (1 )

53 ( 36 )1

Tachikawa Administration Department
Total

10

1

18

20
(

3

1

) Total number of staff of Tachikawa Administration Department.

The number under Technical Staff at the Center for Engineering and Technical Support and Administrative Staff at the Tachikawa Administration Department
include two each staff member who retired because of age but was reemployed in a different position.

Staff ( As of August 1, 2020 )Director-General

Hiroe TSUBAKI

Vice Director-General

Satoshi ITO

Vice Director-General

Satoshi YAMASHITA

Vice Director-General

Yoshihiko MIYASATO

Department of Statistical Modeling
Director

Tomoko MATSUI• Prediction and Control Group ————————————————————————————————————————————————————————————
Prof.

Yoshinori KAWASAKIProf.

Yoshihiko MIYASATO

Prof.

Atsushi YOSHIMOTO

Assoc. Prof.

Jiancang ZHUANG

Assoc. Prof.

Yumi TAKIZAWA

Assoc. Prof.

Fumikazu MIWAKEICHI• Complex System Modeling Group ——————————————————————————————————————————————————————————Prof.

Tomoko MATSUIAssoc. Prof.

Shinsuke KOYAMA

Yukito IBA

Prof.

Prof.

Hideitsu HINO

• Data Assimilation Group ———————————————————————————————————————————————————————————————Prof.

Genta UENO

Assoc. Prof.

Shinya NAKANOAssist. Prof.

Shunichi NOMURA

Visiting Prof.

Shinichi OTANIVisiting Prof.

Masako KAMIYAMA

Visiting Prof.

Tadahiko SATO

Visiting Prof.

Kazuyuki NAKAMURA

Visiting Prof.

Tomoyuki HIGUCHI

Visiting Assoc. Prof. Hiroshi KATO

Visiting Assoc. Prof. Hiromichi NAGAO

Visiting Assoc. Prof. Yosuke FUJII

Visiting Assoc. Prof. Masaya SAITO

Department of Statistical Data Science
Director

Koji KANEFUJI• Survey Science Group —————————————————————————————————————————————————————————————————————
Prof.

Kazuhiro MINAMI

Project Assist. Prof. Kiyohisa SHIBAI

26

Assoc. Prof.

Tadahiko MAEDA

Project Assist. Prof. Le Duc ANH

Assoc. Prof.

Yoo Sung PARK

Project Assist. Prof. Naoko KATO

Organization

Staff
Department of Statistical Data Science
Visiting Prof.

Takatoshi IMADA

Visiting Prof.

Toru KIKKAWA

Visiting Prof.

Yoshimichi SATO

Visiting Prof.

Wataru MATSUMOTO

Visiting Prof.

Kazufumi MANABE

Visiting Prof.

Masahiro MIZUTA

Visiting Assoc. Prof. Koken OZAKI

Visiting Assoc. Prof. Taisuke FUJITA

Visiting Assoc. Prof. Yusuke INAGAKI• Metric Science Group —————————————————————————————————————————————————————————————————
Prof.

Satoshi YAMASHITA

Prof.

Koji KANEFUJI

Prof.

Shigeyuki MATSUIAssoc. Prof.

Ikuko FUNATOGAWA

Assoc. Prof.

Hisashi NOMA

Assist. Prof.

Nobuo SHIMIZU

Project Researcher

Hiroka HAMADA• Structure Exploration Group —————————————————————————————————————————————————————————————Prof.

Ryo YOSHIDA

Assoc. Prof.

Jun ADACHI

Assoc. Prof.

Stephen WU

Assist. Prof.

Daisuke MURAKAMI

Assoc. Prof.

Kenichiro SHIMATANI

Department of Statistical Inference and Mathematics
Director

Satoshi KURIKI• Mathematical Statistics Group ———————————————————————————————————————————————————————————————
Prof.

Satoshi KURIKI

Prof.

Yoshiyuki NINOMIYAProf.

Shuhei MANO

Assoc. Prof.

Shogo KATO

Assoc. Prof.

Takaaki SHIMURA

Assoc. Prof.

Keisuke YANO

Visiting Prof.

Akimichi TAKEMURA

Visiting Prof.

Nobuaki HOSHINO• Learning and Inference Group —————————————————————————————————————————————————————————————
Prof.

Kenji FUKUMIZU

Prof.

Hironori FUJISAWA

Assoc. Prof.

Masayuki HENMI

Assoc. Prof.

Ayaka SAKATA

Assoc. Prof.

Daichi MOCHIHASHI• Mathematical Optimization Group —————————————————————————————————————————————————————————
Prof.

Satoshi ITOProf.

Shiro IKEDA

Assoc. Prof.

Mirai TANAKA

Assoc. Prof.

Bruno FIGUEIRA LOURENÇO

Visiting Prof.

Eitaro AIYOSHI

Visiting Prof.

Kazuo MUROTA

Risk Analysis Research Center
Director

Satoshi YAMASHITA

Vice Director

Shogo KATO

Prof.

Satoshi YAMASHITA

Prof.

Satoshi KURIKI

Prof.

Koji KANEFUJI

Prof.

Tomoko MATSUIProf.

Atsushi YOSHIMOTO

Prof.

Yoshinori KAWASAKIProf.

Yoshiyuki NINOMIYAProf.

Kazuhiro MINAMI

Prof.

Shuhei MANO

Assoc. Prof.

Masayuki HENMI

Assoc. Prof.

Jiancang ZHUANG

Assoc. Prof.

Kenichiro SHIMATANI

Assoc. Prof.

Shogo KATO

Assoc. Prof.

Yumi TAKIZAWA

Assoc. Prof.

Takaaki SHIMURA

Assoc. Prof.

Stephen WU

Project Assoc. Prof. Masayuki KUMON

Assist. Prof.

Shunichi NOMURA

Assist. Prof.

Daisuke MURAKAMI

Project Assist. Prof. Junchao ZHANG

Project Assist. Prof. Hideaki NAGAHATA

Visiting Prof.

Masakazu ANDO

Visiting Prof.

Masao UEKI

Visiting Prof.

Tadashi ONO

Visiting Prof.

Takashi KAMEYA

Visiting Prof.

Toshikazu KITANO

Visiting Prof.

Naoto KUNITOMO

Visiting Prof.

Naoki SAKAI

Visiting Prof.

Yo SHINA

Visiting Prof.

Yasutaka SHIMIZU

Visiting Prof.

Rinya TAKAHASHI

Visiting Prof.

Satoshi TAKIZAWA

Visiting Prof.

Hideatsu TSUKAHARA

Visiting Prof.

Hiroshi TSUDA

Visiting Prof.

Tetsuji TONDA

Visiting Prof.

Shunji HASHIMOTO

27

Risk Analysis Research Center
Visiting Prof.

Hisayuki HARA

Visiting Prof.

Masaaki FUKASAWA

Visiting Prof.

Satoshi FUJII

Visiting Prof.

Toshihiro HORIGUCHI

Visiting Prof.

Toshio HONDA

Visiting Prof.

Mihoko MINAMI

Visiting Prof.

Yoshiki YAMAGATA

Visiting Prof.

Nakahiro YOSHIDA

Visiting Prof.

Toshinao YOSHIBA

Visiting Prof.

Takaaki YOSHINO

Visiting Prof.

Sadaaki MIYAMOTO

Visiting Prof.

Shinsuke ITO

Visiting Prof.

Hitoshi MOTOYAMA

Visiting Prof.

Kazuyuki SUZUKI

Visiting Assoc. Prof. Takaki IWATA

Visiting Assoc. Prof. Bogdan Dumitru ENESCU

Visiting Assoc. Prof. Yukihiko OKADA

Visiting Assoc. Prof. Teppei OGIHARA

Visiting Assoc. Prof. Kenichi KAMO

Visiting Assoc. Prof. Yuta KOIKE

Visiting Assoc. Prof. Masashi KONOSHIMA

Visiting Assoc. Prof. Junichi TAKAHASHI

Visiting Assoc. Prof. Isao TAKABE

Visiting Assoc. Prof. Dou XIAOLING

Visiting Assoc. Prof. Seisho SATO

Visiting Assoc. Prof. Kazuyoshi NANJO

Visiting Assoc. Prof. Takafumi KUBOTA

Visiting Assoc. Prof. Keisuke FUKUI

Research Center for Statistical Machine Learning
Director

Kenji FUKUMIZU

Vice Director

Tomoko MATSUI

Kenji FUKUMIZU

Prof.

Tomoko MATSUIProf.

Yoshihiko MIYASATO

Prof.

Satoshi ITOProf.

Shiro IKEDA

Prof.

Satoshi KURIKI

Prof.

Hironori FUJISAWA

Prof.

Kazuhiro MINAMI

Prof.

Hideitsu HINO

Assoc. Prof.

Daichi MOCHIHASHI

Assoc. Prof.

Shinsuke KOYAMA

Assoc. Prof.

Ayaka SAKATA

Assoc. Prof.

Mirai TANAKA

Project Assoc. Prof. Shinichiro GOTO

Assist. Prof.

Daisuke MURAKAMI

Project Assist. Prof. Yoichi MOTOTAKE

Visiting Prof.

Arthur GRETTON

Visiting Prof.

Takashi TSUCHIYA

Prof.

Project Assist. Prof. Sho SAITO
Visiting Prof.
Visiting Prof.

Masataka GOTO

Visiting Prof.

Katsuki FUJISAWA

Visiting Assoc. Prof. Sayaka SHIOTA

Visiting Assoc. Prof. Tsutomu TAKEUCHI

Visiting Assoc. Prof. Eiji MOTOHASHI

Visiting Assoc. Prof. Masaaki IMAIZUMIVisiting Assoc. Prof. Makoto YAMADA

Yuji SHINANO

Visiting Assoc. Prof. Kei KOBAYASHI

Data Science Center for Creative Design and Manufacturing
Director

Ryo YOSHIDA

Vice Director

Hironori FUJISAWAProf.

Ryo YOSHIDA

Prof.

Hironori FUJISAWA

Prof.

Kenji FUKUMIZU

Prof.

Hideitsu HINO

Assoc. Prof.

Shinya NAKANOAssoc. Prof.

Daichi MOCHIHASHI

Assoc. Prof.

Stephen WU

Project Assist. Prof. Liu CHANG

Project Assist. Prof. Yuta AOKI

Project Assist. Prof. Yoshihiro HAYASHI

Visiting Assoc. Prof. Terumasa TOKUNAGA

Research Center for Medical and Health Data Science
Director

28

Shigeyuki MATSUI

Vice Director

Ikuko FUNATOGAWA

Prof.

Satoshi YAMASHITA

Prof.

Shigeyuki MATSUI

Project Prof.

Shinto EGUCHI

Assoc. Prof.

Hisashi NOMA

Assoc. Prof.

Ikuko FUNATOGAWA

Assoc. Prof.

Masayuki HENMI

Assoc. Prof.

Fumikazu MIWAKEICHI

Project Assoc. Prof. Kengo NAGASHIMA

Visiting Prof.

Yasuo OHASHI

Visiting Prof.

Senichiro KIKUCHI

Visiting Prof.

Ken KIYONO

Visiting Prof.

Atsushi GOTO

Visiting Prof.

Satoshi TERAMUKAI

Visiting Prof.

Hisateru TACHIMORI

Visiting Prof.

Michiko WATANABE

Visiting Prof.

Toshiya SATO

Visiting Prof.

Satoshi HATTORI

Visiting Prof.

Nobuaki NISHIYAMA

Visiting Prof.

Yoichi ITO

Visiting Assoc. Prof. Kunihiko TAKAHASHI

Project Assoc. Prof. Mayumi OKA

Organization

Staff
Research Center for Medical and Health Data Science
Visiting Assoc. Prof. Ryoichi KIMURA

Visiting Assoc. Prof. Yasunori SATO

Visiting Assoc. Prof. Masataka TAGURI

Visiting Assoc. Prof. Kazushi MARUO

School of Statistical Thinking
Director

Yoshinori KAWASAKI

Vice Director

Yukito IBA

Prof.

Yoshinori KAWASAKIProf.

Satoshi ITO

Prof.

Yukito IBA

Prof.

Kenji FUKUMIZU

Prof.

Yoshiyuki NINOMIYAProf.

Hideitsu HINOProf.

Shiro IKEDA

Project Prof.

Yoshiyasu TAMURA

Project Prof.

Junji NAKANO

Project Prof.

Manabu IWASAKI

Assoc. Prof.

Kenichiro SHIMATANI

Assoc. Prof.

Masayuki HENMI

Shunichi NOMURA

Project Assist. Prof. Kazuhei KIKUCHI

Assist. Prof.

Visiting Assoc. Prof. Osamu KOMORI

Visiting Assoc. Prof. Kei TAKAHASHI

Center for Engineering and Technical Support
Director
Deputy Manager

Genta UENO
Yuriko WATANABE

Vice Director

Kazuhiro MINAMI

Unit Leader of Computing Facilities Unit

Mitsuru HAYASAKA

Unit Leader of Computer Networking Unit

Kazuhiro NAKAMURA

Unit Leader of Information Resources Unit

Yuriko WATANABE

Unit Leader of Media Development Unit

Akiko NAGASHIMA

Library
Head

Genta UENO

Administration Planning and Coordination Section
Chief Director
Director of Planning Unit

Hiroe TSUBAKI

Satoshi ITO

Director of Information and Public Relations Unit Satoshi ITO
Director of NOE Promotion Unit

Satoshi YAMASHITA

Director of International Affairs Unit

Tomoko MATSUI

Director of Evaluation Unit

Yoshihiko MIYASATO

Director of Intellectual Property Unit

Satoshi YAMASHITA

Director of Gender Equality Unit

Satoshi ITO• URA Station ——————————————————————————————————————————————————————————————————————

Kozo KITAMURA

Motoi OKAMOTO

Keisuke HONDA

Tachikawa Administration Department
Director Isao OSHIRO• General Affairs Division ————————————————————————————————————————————————————————————————————
Manager Mitsuo HAMADA

Deputy Manager
Deputy Manager

Hisayuki IGARASHI
Yasunori SAITO

Head, General Affairs Team

Ayako NARITA

Head, Personnel Team

Head, Labor Management Team

Yasunori SAITO

Head, Graduate School Team

Akiharu MIYAGI29

StaffTachikawa Administration Department• Financial Division ———————————————————————————————————————————————————————————————————
Manager

Katsuhiro OZAKI

Deputy Manager

Head, General Affairs and Audit Team

Michihito SAKURAI

Head, Budget and Account Settlement Team (NIPR) Yumiko OKAWA

Head, Budget and Account Settlement Team (ISM)

Hiroaki ARAI

Head, Assets Management and Acceptance Team

Akiko MAEKAWA• Accounting Division ————————————————————————————————————————————————————————————————————
Manager

Koji SAKAMOTO

Deputy Manager

Masayuki KOBAYASHI

Specialist

Hiroshi TAKAGIHead, Accounting Team

Ichiro KAWAJI

Head, Contract Team1

Takeshi KUWAHARA

Head, Contract Team2

Toshiaki TAKASAKIHead, Facilities Team

Takuya SAITO• Research Promotion Division ————————————————————————————————————————————————————————————————————
ManagerHead, Research Promotion Team

Kumiko TANSHO

Hideaki ASANO

Deputy Manager

Motokazu TOYODA

Head, Cooperative Research Team

Hitoshi HIRAYAMA

Council of The Institute of Statistical Mathematics ( As of April 1, 2020 )30

Keiko TAKAHASHI

Counselor for Management, Japan Agency for Marine-Earth Science and Technology ( JAMSTEC ) /Director, Yokohama Institute for Earth Sciences ( YES)

Hiroshi MARUYAMAPFN Fellow, Preferred Networks, Inc.

Iguchi SATORU

Professor, Vice-Director General (on Program), National Astronomical Observatory of Japan,
National Institutes of Natural Sciences

Fumiyasu KOMAKI

Professor, Graduate School of Information Science and Technology, The University of Tokyo

Masayuki UCHIDA

Professor, Graduate School of Engineering Science, Osaka University

Hiroshi SAIGO

Professor, Faculty of Political Science and Economics, Waseda University

Yasuhiro OMORI

Professor, Faculty of Economics, University of Tokyo

Kikuo MAEKAWA

Professor, Spoken Language Division,
Director, Center for Corpus Development National Institute for Japanese Language and Linguistics

Hideki ASOH

Director, National Institute of Advanced Industrial Science and Technology
Department of Information Technology and Human Factors Artificial Intelligence Research Center Deputy

Mihoko MINAMI

Professor, Department of Mathematics, Faculty of Science and Technology, Keio University

Satoshi ITOProfessor ( Vice Director-General, ISM )Satoshi YAMASHITA

Professor ( Vice Director-General, ISM )Yoshihiko MIYASATO

Professor ( Vice Director-General, ISM )Tomoko MATSUI

Professor ( Director of Department of Statistical Modeling, ISM )Koji KANEFUJI

Professor ( Director of Department of Statistical Data Science, ISM )Satoshi KURIKI

Professor ( Department of Mathematical Analysis and Statistical Inference, ISM )Genta UENO

Professor ( Director of Center for Engineering and Technical Support, ISM )Yoshinori KAWASAKI

Professor ( Director of School of Statistical Thinking, ISM )Kenji FUKUMIZU

Professor ( Department of Mathematical Analysis and Statistical Inference, ISM )Hironori FUJISAWA

Professor ( Department of Mathematical Analysis and Statistical Inference, ISM )Organization

Cooperative Research Committee ( As of June 1, 2020 )Hiroyuki MINAMI

Professor, Information Initiative Center, Hokkaido University

Kunihiko TAKAHASHI

Professor, M&D Data Science Center, Tokyo Medical and Dental University

Takahiro TSUCHIYA

Professor, School of Data Science, Yokohama City University

Toshikazu KITANO

Professor, Department of Architecture, Nagoya Institute of Technology

Nagatomo NAKAMURA

Professor, Department of Economics, Sapporo Gakuin University

Atsushi YOSHIMOTO

Professor ( Director of Department of Statistical Modeling, ISM )Koji KANEFUJI

Professor ( Department of Data Science, ISM )Shiro IKEDA

Professor ( Department of Mathematical Analysis and Statistical Inference, ISM )Junji NAKANO

Project Professor ( School of Statistical Thinking, ISM )Managing Committee of School of Statistical Thinking ( As of April 1, 2020 )Manabu IWASAKI

Dean/Professor, School of Data Science, Yokohama City University

Toshiyasu MATUSHIMA

Director, Center for Data Science, Waseda University

Hiroshi YAMADA

Professor, Graduate School of Social Sciences, Hiroshima University

Seiji YAMADA

Professor, Digital Content and Media Sciences Research Division, National Institute of Informatics

Junichi SHIOZAKI

Senior Manager, Insight Signal Business Department, Nomura Research Institute, Ltd.

Yoshinori KAWASAKI

Director ( School of Statistical Thinking, ISM)

Yukito IBA

Vice Director ( School of Statistical Thinking, ISM)

Satoshi ITOProfessor ( Vice Director-General, ISM)

Kenichiro SHIMATANI

Associate Professor ( Department of Data Science, ISM)

Research Ethics Review Committee ( As of April 1, 2020 )Specialist on epidemiology and social research

Masayuki KANAI

Professor, School of Human Sciences, Senshu University

Specialist on epidemiology and social research

Keiko SATO

Associate Professor, Department of Health Informatics Graduate School of
Medicine & School of Public Health, Kyoto University

Specialist in the field of ethics and law

Hitomi NAKAYAMA

Lawyer, Kasumigaseki-Sogo Law Offices

Person in citizen’s position

Yutaka KURIKI

Kindergarten Director, Nishikokubunji Nursery School

Research education staff of ISM

Tadahiko MAEDA

Associate Professor ( Department of Data Science, ISM )Research education staff of ISM

Yoo Sung PARK

Associate Professor ( Department of Data Science, ISM )Research education staff of ISM

Koji KANEFUJI

Professor ( Department of Data Science, ISM )Research education staff of ISM

Ikuko FUNATOGAWA

Associate Professor ( Department of Data Science, ISM )Research education staff of ISM

Hisashi NOMA

Associate Professor ( Department of Data Science, ISM )Professor Emeritus ( As of April 1, 2020 )Sigeki NISIHIRA

Tatsuzo SUZUKI

Giitiro SUZUKI

Ryoichi SHIMIZU

Noboru OHSUMI

Masakatsu MURAKAMI

Kunio TANABE

Tadashi MATSUNAWA

Masami HASEGAWA

Yoshiyuki SAKAMOTO

Takemi YANAGIMOTO

Yoshiaki ITOH

Yasumasa BABA

Katsuomi HIRANO

Masaharu TANEMURA

Makio ISHIGURO

Yosihiko OGATA

Hiroe TSUBAKI

Genshiro KITAGAWA

Nobuhisa KASHIWAGI

Takashi NAKAMURA

Yoshiyasu TAMURA

Tomoyuki HIGUCHI

Junji NAKANO

Ryozo YOSHINO

Shinto EGUCHI

31

History

1944

June

Based on a proposal submitted at an academic study conference in December 1943, the organization was
founded as an institute under the direct control of the Ministry of Education. This proposal aimed to provide
supervision for studies looking into the mathematical principles of probability and their application, and was
also intended to facilitate, unify and promote the publication of research results.

1947

April

The affiliated statistical specialists’ school was opened.

May

The Institute was divided into the 1st Research Dept. ( fundamental theories ), the 2nd Research Dept. (statisticaltheories for the natural sciences), and the 3rd Research Dept. ( statistical theories for the social sciences ).

1949

June

The Institute was placed under the control of the Ministry of Education because of the enforcement of the Ministryof Education Establishment Law.

1955

September

Reorganized into the 1st Research Dept. ( fundamental theories ), the 2nd Research Dept. ( natural and social
science theories ), and the 3rd Research Dept. ( operations research, statistical analysis theories ). The laboratory
system, comprising 9 laboratories and the research guidance promotion room, was adopted.

1969

October

1971

April

The 4th Research Dept. (informatics theories) was instituted.

1973

April

The 5th Research Dept. (prediction and control theories) was instituted.

1975

October

1979

November

1985

April

Repositioned as a National Inter-University Research Institute due to the regulation change. The new mission
includes providing facilities and skills to other universities, in addition to conducting cutting-edge research on
statistical mathematics. Accordingly, the institute was reorganized into four basic research departments (Fundamental Statistical Theory, Statistical Methodology, Prediction & Control, and Interdisciplinary Statistics ) andtwo strategic centers ( Statistical Data Analysis Center and Statistical Education & Information Center ). The
Statistical Technical Training Center was terminated.

1988

October

The Dept. of Statistical Science was instituted in the School of Mathematical and Physical Science, part of the
Graduate University for Advanced Studies ( SOKENDAI ).

1989

June

The Institute was reorganized as an Inter-University Research Institute based on the National School Establishment Law.

1993

April

The Planning Coordination Chief System was instituted.

1997

April

The affiliated Statistical Data Analysis Center was reorganized into the Center for Development of StatisticalComputing, and the Statistical Education and Information Center was reorganized into the Center for Information
on Statistical Sciences.

2003

September

2004

April

The Institute was reorganized into the Institute of Statistical Mathematics, part of the Research Organi­zation ofInformation and Systems of the Inter-University Research Institute based on the National University Corporation Law. The Planning Coordination Chief System was abolished and the position of Vice Director-General was
instituted instead. The Dept. of Statistical Science in the School of Mathe­matical and Physical Science, SOKENDAI, was reorganized. In addition, the Dept. of Statistical Science and the School of Multidisciplinary Scienceswere instituted.

2005

April

The research organization was reorganized into three research departments ( the Department of Statis­tical
Modeling, the Department of Data Science, and the Department of Mathematical Analysis and Statistical Inference ). The affiliated Center for Development of Statistical Computing, the Center for Information on Statistical
Sciences, and the Engineering and Technical Services Section were inte­grated into the Center for Engineeringand Technical Support. The Risk Analysis Research Center was instituted.

2006

April

The Administration Planning Coordination Unit was instituted.

2008

April

The Research Innovation Center was instituted.

A new office building was constructed in Minato Ward.

The 6th Research Dept. (statistical theories of human behavior) was instituted.
The Information Research Building was constructed.

The Prediction and Knowledge Discovery Research Center was instituted.

The Administration Planning and Coordination Unit was reorganized into the Administration Planning and Coordination Section ( hereafter APCS), within which the Intellectual Property Unit, the Evaluation Unit and the Information and Public Relations Unit were instituted.
2009

32

January

The Planning Unit was instituted within APCS.

October

The Institute was moved to 10-3 Midori-cho, Tachikawa, Tokyo.

2010

June

Officially opened the Akaike Guest House.

July

Reorganized the Administration Office to create the NIPR/ISM Joint Administration Office and launch the General
Service Center. The NOE Forwarding Unit ( now we call “NOE Promotion Unit” ) was instituted within APCS.

2011

January

Research and Development Center for Data Assimilation was instituted.
Survey Science Center was instituted.

2012

January

Research Center for Statistical Machine Learning, Service Science Research Center and School of StatisticalThinking were instituted.

2014

July
December

2017

The URA Station was instituted within the Planning Unit.
The Office of Female Researcher Development was instituted within the Planning Unit.

January

Survey Science Center and Service Science Research Center were closed.

July

Data Science Center for Creative Design and Manufacturing was instituted.

December

The International Affairs Unit were instituted, and the Gender Equality Unit, which had been within the Planning
Unit, reorganized within APCS.
The Intellectual Property Unit was reorganized as the Industry-Academia Collaboration and Intellectual Property
Unit within APCS.

2018

April

Research Center for Medical and Health Data Science was instituted.
The NIPR/ISM Joint Administration Office was reorganized as the Tachikawa Administration Department of
the Research Organization of Information and Systems ( ROIS ).

2019

March

Research and Development Center for Data Assimilation was closed.

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